K Number
K133677
Date Cleared
2014-10-16

(321 days)

Product Code
Regulation Number
892.1750
Panel
RA
Reference & Predicate Devices
AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
Intended Use

syngo. CT Single Source Dual Energy is designed to operate with CT images which have been acquired with Siemens Single Source scanners. The various materials of an anatomical region of interest have different attenuation coefficients, which depend on the used energy. These differences provide information on the chemical composition of the scanned body materials. syngo.CT Single Source Dual Energy combines images acquired with low and high energy spectra to visualize this information. Depending on the region of interest, contrast agents may be used.

The functionality of the syngo.CT Single Source Dual Energy applications is as follows:

  • · Monoenergetic
  • Gout Evaluation
  • · Brain Hemorrhage
  • · Liver VNC
  • · Monoenergetic Plus
  • Bone Marrow
  • · Kidney Stones*)

*) Kidney Stones is designed to support the visualization of the chemical composition of kidney stones and especially the differentiation between uric acid stones. For full identification of the kidney stone additional clinical information should be considered such as patient history and urine testing. Only a well-trained radiologist can make the final diagnosis under consideration of all available information. The accuracy of identification is decreased in obese patients.

Device Description

syngo.CT Single Source Dual Energy Software Package is a post processing application package consisting of several post processing application classes that can be used to improve visualization of various materials in the human body.

syngo.CT Single Source Dual Energy is a post processing software package designed to operate on the most recent version syngo.via client server platform, which supports preprocessing and loading of datasets by syngo.via depending on configurable rules.

After loading the two reconstructed image datasets acquired with two different X-ray spectra into syngo.CT Single Source Dual Energy, a registration is performed in case the image data sets are not acquired simultaneously, to compensate for potential motion effects. They are then displayed using linear blending with selectable mixing ratio and color scale. Multiplanar reformations (MPR) of the volume are shown in 3 image segments, which are initialized as sagittal, coronal and axial view.

After arriving at an initial diagnosis on the basis of the CT-images, the user can choose one of the following application classes:

  • Gout Evaluation
  • . Monoenergetic
  • Brain Haemorrhage
  • LiverVNC
  • Kidney Stones
  • . Monoenergetic Plus
  • Bone Marrow

These application classes are designed for specific clinical tasks, so that algorithms, additional tool buttons, the use of colored overlay images and image representation (for example MPR or maximum intensity projection) are optimized correspondingly.

AI/ML Overview

The Siemens syngo.CT Single Source Dual Energy software package (K133677) has a broad indication for use, stating it can aid in the visualization of the chemical composition of various materials in the human body by combining CT images acquired with low and high energy spectra. It lists several specific applications: Monoenergetic, Gout Evaluation, Brain Hemorrhage, Liver VNC, Monoenergetic Plus, Bone Marrow, and Kidney Stones.

For the Kidney Stones application, the indication for use specifies it is "designed to support the visualization of the chemical composition of kidney stones and especially the differentiation between uric acid stones." It further states that "accuracy of identification is decreased in obese patients" and emphasizes that "Only a well-trained radiologist can make the final diagnosis under consideration of all available information."

Here's an analysis of the acceptance criteria and study information provided in the document:

1. Table of Acceptance Criteria and Reported Device Performance

Acceptance Criteria (Stated or Implied)Reported Device Performance
Kidney Stones Application: Ability to differentiate between uric acid and non-uric acid stones.Kidney Stones Application: "The result of this study demonstrated that application Kidney Stones is able to differentiate between uric acid and non-uric acid stones."
Other Application Classes (Monoenergetic, Gout Evaluation, Brain Haemorrhage, Liver VNC, Monoenergetic Plus, Bone Marrow): Functionality of the application classes.Other Application Classes: "Furthermore clinical data have been used to demonstrate the functionality of the other application classes."
Continued conformance with special controls for medical devices containing software.Stated compliance with several IEC and ISO standards (IEC 60601-1-6, IEC 60601-1-4, IEC 62304, ISO 14971, DICOM). "Non clinical tests (integration and functional) and phantom testing were conducted..."
All software specifications met acceptance criteria."The testing results supports that all the software specifications have met the acceptance criteria."
Verification and validation of the device found acceptable to support claims of substantial equivalence."Testing for verification and validation of the device was found acceptable to support the claims of substantial equivalence."

2. Sample Size Used for the Test Set and Data Provenance

  • Sample Size (Test Set): For the Kidney Stones application, a "phantom study" was performed, but the specific number of phantoms or stone types tested is not provided. For the "other application classes," "clinical data" was used, but the sample size (number of patients/cases) and whether this data was retrospective or prospective is not specified.
  • Data Provenance: Not explicitly stated for either the phantom study or the clinical data. The document does not mention the country of origin. The clinical data appears to be retrospective as it's used to demonstrate functionality rather than being part of a controlled prospective trial described in detail.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

  • This information is not provided in the document. While the indication for use mentions that "Only a well-trained radiologist can make the final diagnosis," it does not detail the number or qualifications of experts involved in establishing ground truth for the performance studies.

4. Adjudication Method for the Test Set

  • The document does not specify any adjudication method for the test set.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

  • No, a multi-reader multi-case (MRMC) comparative effectiveness study was not done or reported in this document. The document focuses on the performance of the device itself rather than its impact on human reader performance.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

  • Yes, the performance studies described appear to be standalone (algorithm only). For the Kidney Stones application, a "phantom study was performed to test the ability of application kidney stones." For other applications, "clinical data have been used to demonstrate the functionality of the other application classes." These descriptions imply direct evaluation of the software's output rather than assessing human performance with or without the software.

7. The Type of Ground Truth Used

  • For the Kidney Stones application, the ground truth was likely based on the known composition of the phantom stones ("phantom study... differentiate between uric acid and non-uric acid stones").
  • For the "other application classes," the "clinical data" used to demonstrate functionality would have had an associated ground truth, but the specific type (e.g., pathology, expert consensus, outcomes data) is not specified.

8. The Sample Size for the Training Set

  • The document does not provide any information regarding the sample size for a training set. The descriptions focus on verification and validation testing, not the development or training of the algorithms. Given that this is a 510(k) for an updated software package, it's possible that the core algorithms were developed using proprietary datasets not disclosed here, or that the system relies on physical principles rather than extensive machine learning requiring a distinct "training set" in the modern sense.

9. How the Ground Truth for the Training Set Was Established

  • As the document does not mention a training set, it also does not provide information on how its ground truth was established.

§ 892.1750 Computed tomography x-ray system.

(a)
Identification. A computed tomography x-ray system is a diagnostic x-ray system intended to produce cross-sectional images of the body by computer reconstruction of x-ray transmission data from the same axial plane taken at different angles. This generic type of device may include signal analysis and display equipment, patient and equipment supports, component parts, and accessories.(b)
Classification. Class II.